Package: sparsenetgls
Type: Package
Title: Using Gaussian graphical structue learning estimation in generalized least squared regression for multivariate normal regression 
Version: 1.29.0
Authors@R: c(person("Irene", "Zeng", role = c("aut", "cre"),
            email = "szen003@aucklanduni.ac.nz"),
            person("Thomas", "Lumley", role = "ctb", email = "t.lumey@auckland.ac.nz"))
Description: The package provides methods of combining the graph structure learning and generalized 
    least squares regression to improve the regression estimation. The main function sparsenetgls() provides
	solutions for multivariate regression with Gaussian distributed dependant variables and explanatory variables
    utlizing multiple well-known graph structure learning approaches to estimating the precision matrix, and uses
	a penalized variance covariance matrix with a distance tuning parameter of the graph structure in deriving the
    sandwich estimators in generalized least squares (gls) regression. This package also provides functions for 
    assessing a Gaussian graphical model which uses the	penalized approach. It uses Receiver Operative Characteristics
    curve as a visualization tool in the assessment.  
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 4.0.0), Matrix, MASS  
Imports: methods, glmnet, huge, stats, graphics, utils 
Suggests: testthat,
    lme4,
    BiocStyle, 
    knitr,
    rmarkdown,
    roxygen2  (>= 5.0.0)
NeedsCompilation: no
URL: 
RoxygenNote: 6.0.1
biocViews: ImmunoOncology, GraphAndNetwork,Regression,Metabolomics,CopyNumberVariation,MassSpectrometry,Proteomics,Software,Visualization
bugReport: https://github.com/superOmics/sparsenetgls/issues
VignetteBuilder: knitr
SystemRequirements: GNU make
git_url: https://git.bioconductor.org/packages/sparsenetgls
git_branch: devel
git_last_commit: 92f4ff1
git_last_commit_date: 2025-10-29
Repository: Bioconductor 3.23
